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International Conference on Extending Database Technology

EDBT 2002: XML-Based Data Management and Multimedia Engineering — EDBT 2002 Workshops pp 365–375Cite as

Towards Enhanced Compression Techniques for Efficient High-Dimensional Similarity Search in Multimedia Databases

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2490))

Abstract

In this paper, we introduce a new efficient compression technique for high-dimensional similarity search in MMDBS. We propose the Active Vertice Tree which is based on concave cluster geometries. Furthermore, we briefly sketch a model for high-dimensional point alignments and specify basic requirements for high-dimensional cluster shapes. Finally, we compare the Active Vertice Tree with other methods for high-dimensional similarity search in terms of their retrieval performance.

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References

  1. S. Balko and I. Schmitt. Active Vertice Clusters-A Sophisticated Concave Cluster Shape Approach for Efficient High Dimensional Nearest Neighbor Retrieval. Preprint 24, Fakultät für Informatik, Universität Magdeburg, 2001.

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  2. S. Balko and I. Schmitt. Concave Cluster Shapes for Efficient Nearest Neighbor Search in High Dimensional Space. Preprint 23, Fakultät für Informatik, Universität Magdeburg, 2001.

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  7. I. Schmitt. Nearest Neighbor Search in High Dimensional Space by Using Convex Hulls. Preprint 6, Fakultät für Informatik, Universität Magdeburg, 2001.

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  8. R. Weber, H.-J. Schek, and S. Blott. A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces. In A. Gupta et al., editor, Proc. of VLDB, pages 194–205. Morgan Kaufmann Publishers, San Francisco, CA, 1998.

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Author information

Authors and Affiliations

  1. Database Research Group, Institut für Technische und Betriebliche Informationssysteme, Germany

    Sören Balko & Ingo Schmitt

  2. Fakultät für Informatik, Universität Magdeburg, Germany

    Gunter Saake

Authors
  1. Sören Balko
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  2. Ingo Schmitt
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  3. Gunter Saake
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Editor information

Editors and Affiliations

  1. IBM developer Works, 6 New Square, Bedfont Lakes, Feltham, Middlesex, TW14 8HA, UK

    Akmal B. Chaudhri

  2. Institute for Computer Science, University of Essen, Schützenbahn 70, 45117, Essen, Germany

    Rainer Unland

  3. IRIN, Nantes University, 2, rue de la Houssiniére, 44322, Nantes, France

    Chabane Djeraba

  4. Department of Computer Science DBIS Research Group, University of Rostock, 18051, Rostock, Germany

    Wolfgang Lindner

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© 2002 Springer-Verlag Berlin Heidelberg

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Cite this paper

Balko, S., Schmitt, I., Saake, G. (2002). Towards Enhanced Compression Techniques for Efficient High-Dimensional Similarity Search in Multimedia Databases. In: Chaudhri, A.B., Unland, R., Djeraba, C., Lindner, W. (eds) XML-Based Data Management and Multimedia Engineering — EDBT 2002 Workshops. EDBT 2002. Lecture Notes in Computer Science, vol 2490. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36128-6_21

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  • DOI: https://doi.org/10.1007/3-540-36128-6_21

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00130-0

  • Online ISBN: 978-3-540-36128-2

  • eBook Packages: Springer Book Archive

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